Chemotherapy treatment for cancer is a complex optimisation problem with a large number of interacting variables and constraints. A number of different probabilistic algorithms have been applied to it with varying success. In this paper we expand on this by applying two estimation of distribution algorithms to the problem. One is UMDA, which uses a univariate probabilistic model similar to previously applied EDAs. The other is hBOA, the first EDA using a multivariate probabilistic model to be applied to the chemotherapy problem. While instinct would lead us to predict that the more sophisticated algorithm would yield better performance on a complex problem like this, we show that it is outperformed by the algorithms using the simpler univariate model. We hypothesise that this is caused by the more sophisticated algorithm being impeded by the large number of interactions in the problem which are unnecessary for its solution.

翻译：癌症的化疗治疗是一个复杂的优化问题,有许多相互作用的变数和限制因素。 一些不同的概率算法已经成功地应用到它身上。 在本文中,我们通过对问题应用两种分配算法来扩大这一点。 一个是UMADA, 它使用一种类似于以前应用的EDAs的单象牙性概率模型。 另一个是HBOA, 这是第一个对化疗问题应用多种变数性能模型的EDA。 虽然本能会让我们预测更精密的算法在这样复杂的问题上产生更好的性能,但我们表明它比使用较简单的单牙模型的算法表现得还要好。 我们假设这是由问题中大量互动所阻碍的更复杂的算法造成的,而这些问题的解决是不必要的。